Jie Cao

Date of Award

5-2015

Level of Access Assigned by Author

Campus-Only Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Marine Biology

Advisor

Yong Chen

Second Committee Member

Huijie Xue

Third Committee Member

Gayle Zydlewski

Abstract

Fishery stock assessment is an essential scientific process to provide scientific advice for sustainable utilization of marine resources. The quality of a stock assessment (i.e., the accuracy and precision of stock size and exploitation rate estimates) is related to the quality and quantity of the input data and the sophistication of the stock assessment model. In this dissertation four case studies were conducted to address and evaluate the potential problems in data collection and modeling, with the aim of providing suggestions and solutions to improve fisheries stock assessment.

The performances of different survey designs were evaluated for estimating the American lobster density over time in the Gulf of Maine (GOM). The results show that stratified random sampling had stable performance across years and seasons. Additionally, appropriate stratification, such as using depth to determine strata, significantly improved the precision and efficiency over simple random sampling.

Incorrect assumptions made in a model, either implicitly or explicitly, can have a substantial impact on stock assessment results and management advice. Misspecification of fishery selectivity, natural mortality and fishery catchability in the assessment model were investigated for the Atlantic herring stock assessment. The results show that misspecifications of key population dynamic processes in modeling could cause retrospective error. Most fish populations exhibit complex spatial structure. However, the assessment of fish populations has historically relied on assumptions that the individuals within a region of interest belong to a "unit stock". Impacts of ignoring spatial structure were quantified for the assessment of Atlantic cod in the GOM. The results show that, while stock mixing causes measurable bias in the stock assessment model, under the conditions we tested in this study, the model still performed well.

A size-structured assessment model dedicated to protandric hermaphrodite pandalid stock assessment was developed, with the aim of increasing the biological realism. Simulations were conducted to evaluate the performance of the new model. The results suggest that the newly developed assessment model could greatly improve the shrimp assessment by considering environmentally-driven recruitment dynamics and seasonality in the fishery and growth pattern.

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